Search

Water – Global Assessment and Prognosis (WaterGAP) is a modeling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms, and new data of the most recent model version v2.2e are described, together with a thorough evaluation of the simulated water use, streamflow, and terrestrial water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small human-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended data set of streamflow observations at 1509 gauging stations. The modifications resulted in a small decrease in the estimated global renewable water resources. The model can now be started using prescribed water storages and other conditions, facilitating data assimilation and near-real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration, or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described, and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework. [ABSTRACT FROM AUTHOR]

Considered the Asian water tower, the Qinghai–Tibet Plateau (QTP) processes substantial permafrost, and its hydrological environments are spatially different and can be easily disturbed by changing permafrost and melting ground ice. Permafrost degradation compels melting permafrost to become an important source of surface runoff, changes the storage of groundwater, and greatly influences the hydrological processes in permafrost regions. However, the evidence linking permafrost degradation and hydrological processes on the QTP is lacking, which increases the uncertainties in the evaluation of the results of changing permafrost on the water resources. Stable isotopes offer valuable information on the connections between changing permafrost (ground ice) and water components. It is therefore particularly important to observe the changes in the stable isotopes of different waterbodies which can vary over hourly to annual timescales and truly capture the thawing signals and reflect the influence of permafrost (ground ice) on the regional hydrological processes. The Beiluhe Basin (BLH) in the hinterland of the QTP was selected; it integrates all the water components related to hydrological cycles well and is an ideal site to study the hydrological effect of permafrost change. This paper presents the temporal data of stable isotopes (δ18 O, δ D, and d -excess) in different waterbodies (precipitation, stream water, thermokarst lake, and groundwater) in the BLH produced between 2017 and 2022. Here, the first detailed stable isotope data of ground ice at 17 boreholes and 2 thaw slumps are presented. A detailed description of the sampling processes, sample pretreating processes, and isotopic data quality control is given. The data first described the full seasonal isotope amplitude in the precipitation, stream, and thermokarst lakes and delineated the depth isotopic variability in ground ice. In total, 554 precipitation samples, 2402 lake/pond samples, 675 stream water samples, 102 supra-permafrost water samples, and 19 sub-permafrost water samples were collected during 6 years of continuous sampling work. Importantly, 359 ground ice samples at different depths from 17 boreholes and 2 profiles were collected. This first dataset provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP. It also provides support for the cryospheric study on the Northern Hemisphere. The dataset provided in this paper can be obtained at 10.5281/zenodo.10684110 (Yang, 2024). [ABSTRACT FROM AUTHOR]

Using the peak ground acceleration (PGA) under four exceedance probabilities calculated via a probabilistic seismic hazard analysis method for China, the probability density function of PGA was obtained by fitting with the Cornell seismic hazard exceedance probability PGA function model. Combined with the seismic fragility function of the water supply system calculated on the basis of the empirical matrix of actual earthquake damage and the exposure of fixed assets, the expected seismic loss and expected loss rate models of the water supply system were established, and the classification standard with the expected seismic loss rate of the water supply system as the index was proposed. The seismic fragility of the water supply system was classified, and the exposure of the water supply system was analyzed. The total fixed assets in the Statistical Yearbook of Urban Water Supply, henceforth called Water Supply Yearbook, were taken as the exposure to earthquake in the region. The accuracy of the fragility model in this paper was verified through the actual earthquake damage losses in Deyang. Taking the water supply system of 720 cities in mainland China as an example, the distribution maps of expected seismic loss and expected loss rate were calculated and drawn. The expected loss rate model was verified by the key earthquake prevention areas in mainland China. The assessment model based on the expected loss and expected loss rate was taken as the seismic risk assessment model of water supply systems in mainland China. [ABSTRACT FROM AUTHOR]

Understanding past and present hydrosystem feedbacks to global ocean–atmospheric interactions represents one of the main challenges to preventing droughts, extreme events, and related human catastrophes in the face of global warming, especially in arid and semiarid environments. In eastern Africa, the El Niño–Southern Oscillation (ENSO) was identified as one of the primary drivers of precipitation variability affecting water availability. However, the northern East African Rift System (EARS) still suffers from the underrepresentation of predictive and ENSO teleconnection models because of the scarcity of local to regional historical or palaeo-data. In this paper, we provide a 50-year seasonal flood and drought chronicle of the Awash River catchment from the study of laminated sediment from Gemeri and Afambo lakes (central Afar region, Ethiopia) with the aim of reconstructing the magnitude of regional hydroclimatic events. Pluricentimetric micro-laminated lithogenic facies alternating with plurimillimetric carbonate-enriched facies are investigated in both lakes. We couple dating methods including radiocarbon, short-lived radionuclides, palaeomagnetic field variations, and varve counting on both lake deposits to build a high-resolution age model and to discuss the regional hydrosedimentary dynamics of the Awash River over the last ∼ 700 years with a focus on the last 50 years. Using a multiproxy approach, we observe that following a multicentennial enhanced hydrological period, the two lakes have experienced a gradual decrease in river load inflow since 1979 CE, attaining extreme drought and high evaporative conditions between 1991 and 1997 CE. In 2014, the construction of a dam and increased agricultural water management in the lower Awash River plain impacted the erodibility of local soils and the hydrosedimentary balance of the lake basins, as evidenced by a disproportionate sediment accumulation rate. Comparison of our quantitative reconstruction with (i) lake water surface evolution, (ii) the interannual Awash River flow rates, and (iii) the El Niño 3.4 model highlights the intermittent connections between ENSO sea surface temperature anomalies, regional droughts, and hydrological conditions in the northern EARS. [ABSTRACT FROM AUTHOR]

In the context of climate change, it is important to monitor the dynamics of the Earth's surface in order to prevent extreme weather phenomena such as floods and droughts. To this end, global meteorological forecasting is constantly being improved, with a recent breakthrough in deep learning methods. In this paper, we propose to adapt a recent weather forecasting architecture, called GraphCast, to a water resources forecasting task using high-resolution satellite image time series (SITS). Based on an intermediate mesh, the data geometry used within the network is adapted to match high spatial resolution data acquired in two-dimensional space. In particular, we introduce a predefined irregular mesh based on a segmentation map to guide the network's predictions and bring more detail to specific areas. We conduct experiments to forecast water resources index two months ahead on lakes and rivers in Italy and Spain. We demonstrate that our adaptation of GraphCast outperforms the existing frameworks designed for SITS analysis. It also showed stable results for the main hyperparameter, i.e., the number of superpixels. We conclude that adapting global meteorological forecasting methods to SITS settings can be beneficial for high spatial resolution predictions. [ABSTRACT FROM AUTHOR]

A cascade of drought impacts refers to a series of interconnected events that trigger a chain reaction of impacts, extending beyond water scarcity, to affect agricultural production, socio-economic factors, and the environment. This paper aims to understand the role of society in mitigating drought impacts, particularly through policy responses. Conducting a case study in Ceará state, northeast Brazil, we used a global rare dataset of continuously drought monitoring, complemented by interviews with smallholder farmers and agricultural extension technicians. Additionally, we analyzed policy documents related to public policies implemented at the local level. Employing a classification of drought impacts as our analytical framework, our findings indicate that socio-environmental-economic impacts of drought are less frequently reported, suggesting that development policies are mitigating cascading effects on livelihoods. Most impacts are associated with hydrological impacts of drought, suggesting unintended consequences of investments in increasing water supply. We emphasize the significant contribution of public policies to mitigating the cascading effects of drought, which do not necessarily involve increasing water availability, but strengthen the local economy. [ABSTRACT FROM AUTHOR]

The water supply of water resources allocation under extreme drought is insufficient, and the limited available water resources make it urgent to tap the potential of water supply. In this paper, the Yellow River water supply area in Henan Province is taken as an example to study the multi-water source allocation under extreme drought. According to the Palmer Drought Severity Index (PDSI), the extreme drought years are selected, and the water supply and demand balance in the extreme drought years is analyzed, and the water shortage degree of each water supply area is obtained. In this paper, unconventional water, flood resource utilization and elastic exploitation of groundwater are used as potential water sources. Different water supply scenarios are set up according to different potential tapping measures, and multi-scenario supply increase under extreme drought is explored. A multi-water source allocation model with the goal of minimizing water shortage is constructed, and a multi-scenario supply increase allocation scheme is proposed, which provides a basis for the study of water supply increase allocation to alleviate the drought degree of the the Yellow River Water Supply Area in Henan. Through the Multi-scenario potential tapping of multiple water sources, the existing potential water volume can be maximized, which is conducive to reducing the water supply pressure and water use restrictions of conventional water sources, improving the support capacity and guarantee capacity of water resources, and reducing the economic and social development bottlenecks caused by extreme drought. [ABSTRACT FROM AUTHOR]

Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities. [ABSTRACT FROM AUTHOR]

The land surface, hydrological, and groundwater modelling communities all have expertise in simulating the hydrological processes at play in the terrestrial component of the Earth system. However, these communities, and the wider Earth system modelling community, have largely remained distinct with limited collaboration between disciplines, hindering progress in the representation of hydrological processes in the land component of Earth system models (ESMs). In order to address key societal questions regarding the future availability of water resources and the intensity of extreme events such as floods and droughts in a changing climate, these communities must come together and build on the strengths of one another to produce next-generation land system models that are able to adequately simulate the terrestrial water cycle under change. The development of a common modelling infrastructure can contribute to stimulating cross-fertilisation by structuring and standardising the interactions. This paper presents such an infrastructure, a land system framework, which targets an intermediate level of complexity and constrains interfaces between components (and communities) and, in doing so, aims to facilitate an easier pipeline between the development of (sub-)community models and their integration, both for standalone use and for use in ESMs. This paper first outlines the conceptual design and technical capabilities of the framework; thereafter, its usage and useful characteristics are demonstrated through case studies. The main innovations presented here are (1) the interfacing constraints themselves; (2) the implementation in Python (the Unified Framework for Hydrology, unifhy); and (3) the demonstration of standalone use cases using the framework. The existing framework does not yet meet all our goals, in particular, of directly supporting integration into larger ESMs, so we conclude with the remaining limitations of the current framework and necessary future developments. [ABSTRACT FROM AUTHOR]

This paper describes GCAM-USA v5.3_water_dispatch, an open-source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (US) and represents the US economy and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land use regions, and 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of US water demands and supplies and sub-annual operations (day and night for each month) in the US electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for (and production of) energy, water, and crops at a subnational level in the United States while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land use outcomes. We conclude with information about how public users can access the model. [ABSTRACT FROM AUTHOR]

Water - Global Assessment and Prognosis (WaterGAP) is a modelling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms and new data of the most recent model version v2.2e are described, together with a thorough evaluation of simulated water use, streamflow and total water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small man-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended dataset of streamflow observations at 1509 gauging stations. The model can now be started using pre-scribed water storages and other conditions, which facilitates data assimilation as well as near real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework. [ABSTRACT FROM AUTHOR]

Streamflow drought hazard indicators (SDHIs) are mostly lacking in large-scale drought early warning systems (DEWSs). This paper presents a new systematic approach for selecting and computing SDHIs for monitoring drought for human water supply from surface water and for river ecosystems. We recommend considering the habituation of the system at risk (e.g., a drinking water supplier or small-scale farmers in a specific region) to the streamflow regime when selecting indicators; i.e., users of the DEWSs should determine which type of deviation from normal (e.g., a certain interannual variability or a certain relative reduction of streamflow) the risk system of interest has become used to and adapted to. Distinguishing four indicator types, we classify indicators of drought magnitude (water anomaly during a predefined period) and severity (cumulated magnitude since the onset of the drought event) and specify the many relevant decisions that need to be made when computing SDHIs. Using the global hydrological model WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For large-scale DEWSs based on the output of hydrological models, we recommend specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for informing reservoir managers. These SDHIs include both drought magnitude and severity indicators that differ by the temporal averaging period and the habituation of the risk system to reduced water availability. Depending on the habituation of the risk system, drought magnitude is best quantified either by the relative deviation from the mean or by the return period of the streamflow value that is based on the frequency of non-exceedance. To compute the return period, we favor empirical percentiles over the standardized streamflow indicator as the former do not entail uncertainties due to the fitting of a probability distribution and can be computed for all streamflow time series. Drought severity should be assessed with indicators that imply habituation to a certain degree of interannual variability, to a certain reduction from mean streamflow, and to the ability to fulfill human water demand and environmental flows. Reservoir managers are best informed by the SDHIs of the grid cell that represents inflow into the reservoir. The DEWSs must provide comprehensive and clear explanations about the suitability of the provided indicators for specific risk systems. [ABSTRACT FROM AUTHOR]

The prediction of water resource evolution is considered to be a major challenge for the coming century, particularly in the context of climate change and increasing demographic pressure. Water resources are directly linked to the continental water cycle, and the main processes modulating changes can be represented by global hydrological models. However, anthropogenic impacts on water resources, and in particular the effects of dams-reservoirs on river flows, are still poorly known and generally neglected in coupled land surface–river routing models. This paper presents a parameterized reservoir model, DROP (Dam-Reservoir OPeration), based on Hanasaki's scheme to compute monthly releases given inflows, water demands and the management purpose. With its significantly anthropized river basins, Spain has been chosen as a study case for which simulated outflows and water storage variations are evaluated against in situ observations over the period 1979–2014. Using a default configuration of the reservoir model, results reveal its positive contribution in representing the seasonal cycle of discharge and storage variation, specifically for large-storage capacity irrigation reservoirs. Based on a bounded version of the Nash–Sutcliffe efficiency (NSE) index, called C2M , the overall outflow representation is improved by 43 % in the median. For irrigation reservoirs, the improvement rate reaches 80 %. A comprehensive sensitivity analysis of DROP model parameters was conducted based on the performance of C2M on outflows and volumes using the Sobol method. The results show that the most influential parameter is the threshold coefficient describing the demand-controlled release level. The analysis also reveals the parameters that need to be focused on in order to improve river flow or reservoir water storage modeling by highlighting the difference in the individual effects of the parameters and their interactions depending on whether one focuses on outflows or volume mean seasonal patterns. The results of this generic reservoir scheme show promise for modeling present and future reservoir impacts on the continental hydrology within global land surface–river routing models. [ABSTRACT FROM AUTHOR]

This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

Considered as the Asian water tower, the Qinghai–Xizang Plateau (QXP) processes substantial permafrost, where its hydrological environments are spatially differed and can be easily disturbed by changing permafrost and melting ground ice. Permafrost degradation compels melting permafrost to become an important source of surface runoff, changes the storage of groundwater, and greatly influences the hydrological processes in permafrost regions. However, the evidences linking permafrost degradation and hydrological processes on the QXP are lacking, which increase the uncertainties of the evaluation results of changing permafrost on the water resources. Stable isotopes offer valuable information on the connections between changing permafrost (ground ice) and water components. It is therefore particularly important to observe the changes in the stable isotopes of different waterbodies, which can vary over hourly to annual timescales and truly capture the thawing signals and reflect the influence of permafrost (ground ice) on the regional hydrological processes. The Beiluhe Basin (BLH) in the hinterland of QXP were selected, which well integrates all the water components related to hydrological cycles, and is an ideal site to study hydrological effect of permafrost change. This paper presents the temporal data of stable isotopes (δ18O, δD, and d-excess) in different water bodies (precipitation, stream water, thermokarst lake, and groundwater) in the BLH produced between 2017 and 2022. In special, the first detailed stable isotope data of ground ice at 17 boreholes and 2 thaw slumps are presented. A detailed description of the sampling processes, sample pretreating processes, and isotopic data quality control is given. The data firstly described the full seasonal isotope amplitude in the precipitation, stream, and thermokarst lakes, and delineated the depth isotopic variability in ground ice. Totally, 554 precipitation samples, 2402 lakes/ponds samples, 675 stream water samples, 102 supra-permafrost water samples, and 19 sub-permafrost water samples were collected during six years' continuous sampling work. Importantly, 359 ground ice samples at different depths from 17 boreholes and 2 profiles were collected. This first data set provides a new basis for understanding the hydrological effects of permafrost degradation on the QXP. It also provides supports on the cryospheric study on the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at 1/12 ∘ derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (1/2 ∘) and the other with the new spatial resolution (1/12 ∘). It is shown that, overall, CTRIP at 1/12 ∘ outperforms CTRIP at 1/2 ∘ , demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (10.5281/zenodo.6482906,), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies. [ABSTRACT FROM AUTHOR]

In recent years, the impacts of climate change have become increasingly apparent; Slovakia has witnessed a rise in extreme weather conditions caused by climate change. To address these challenges, we analyze and prepare documents focused on monitoring and issuing drought warnings. These documents, including Slovakia's water resource balance, serve as fundamental materials for water management planning. The water resource balance assesses the relationship between water demands and available resources in the past year, identifying areas and times where water demands exceed supply. Additionally, our analysis provides a detailed map of surface and groundwater abstractions across Slovakia, categorizing total and individual abstractions by sector (e.g., public water supply systems, agriculture, and industry). We present surface and groundwater abstraction averages for the period of the last decade 2013–2022, along with its spatial distribution in Slovak districts. This comprehensive approach enables us to better understand and manage our water resources effectively. [ABSTRACT FROM AUTHOR]

Water management in sub-Saharan African river basins is challenged by an uncertain future climatic, social and economical patterns potentially causing diverging water demands and availability, and by multi-stakeholder dynamics, resulting in evolving conflicts and tradeoffs. In such contexts, a better understanding of the sensitivity of water management to the different sources of uncertainty can support policymakers in identifying robust water supply policies balancing optimality and low vulnerability against likely adverse future conditions. This paper contributes an integrated decision-analytic framework combining an optimization, robustness, sensitivity, and uncertainty analysis to retrieve the main sources of vulnerability to optimal and robust reservoir operating policies across multi-dimensional objective spaces. We demonstrate our approach on the lower Umbeluzi river basin, Mozambique, which an archetypal example of sub-Saharan river basin, where surface water scarcity compounded by substantial climatic variability, uncontrolled urbanization rate, and agricultural expansion are hampering the Pequenos Libombos dam's ability to supply the agricultural, energy, and urban sectors. We adopt an Evolutionary Multi-Objective Direct Policy Search (EMODPS) optimization approach for designing optimal operating policies, whose robustness against social, agricultural, infrastructural, and climatic uncertainties is assessed via robustness analysis. We then implement the generalized likelihood uncertainty estimation (GLUE) and PAWN uncertainty and sensitivity analysis methods for disentangling the main challenges to the sustainability of the operating policies and quantifying their impacts on the urban, agricultural, and energy sectors. Numerical results highlight the importance of a robustness analysis when dealing with uncertain scenarios, with optimal non-robust reservoir operating policies largely being dominated by robust control strategies across all stakeholders. Furthermore, while robust policies are usually vulnerable only to hydrological perturbations and are able to sustain the majority of population growth and agricultural expansion scenarios, non-robust policies are sensitive also to social and agricultural changes and require structural interventions to ensure stable supply. [ABSTRACT FROM AUTHOR]

Tropical cyclone precipitation (TCP) has significant impacts on coastal communities through its modulation of flood event frequency as well as the water supply in many regions of the world. Satellite estimates provide our most reliable observations of TCP available globally, however, satellite precipitation estimates were limited because most products only have coverage starting in 1997. In this paper, we present a global dataset of TCP using the newly developed high-resolution Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) which spans from January 1979 to date. This Global Multi-Source Tropical Cyclone Precipitation (MSTCP) dataset is comprised of two main files in the format of tables: the main and profile datasets, both from January 1979 to February 2023. The main file provides various TCP statistics per TC track for using the full record from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, including mean and maximum precipitation rates. The profile dataset comprises the azimuthally averaged precipitation every 10-km away from the center of each storm (until 500 km). The case study of Hurricane Harvey is used to show that MSWEP estimates agree well with another commonly used satellite product. The main statistics of the dataset are analyzed as well, including the differences in the dataset metrics for each of the six TC basins and for each Saffir-Simpson category for storm intensity. [ABSTRACT FROM AUTHOR]

Slushflows are observed worldwide in areas with seasonal snow cover. A regional early warning for slushflow hazard was established in Norway in 2013-14 as the first in the world of its kind and has been operational since. This paper presents a methodology using water supply -- snow depth ratio by snow type, employing grid values and data from historical slushflows, to assess regional slushflow hazard. In Norway slushflows pose a significant natural hazard. Hazard prediction and early warning is therefore crucial to prevent casualties and damage to infrastructure. A benefit with this approach is that it can be implemented in other regions with slushflow hazard where the necessary input data are available. Slushflows are rapid mass movements of water saturated snow. They release in low to moderate slopes (< 30°). Due to their high liquid water content, slushflows usually have long runouts, and they can transform into debris flows. A complex interaction between several factors is the key to slushflow initiation. Impeded infiltration of the ground is a prerequisite. Porous snow structures are most prone to destabilization. Rate and duration of water supply, due to rain on snow and/or intense snowmelt, is crucial. The daily assessment of slushflow hazard is based on information on snow cover and hydro-meteorological conditions. Four main variables are central: ground conditions, snow properties, air temperature, and water supply to snow. A wide range of meteorological and hydrological parameters from multiple sources, together with real-time data from automatic stations and observations from the field, are assessed. The data is provided from the decision-making tool Varsom Xgeo, presenting outputs from model simulations as gridded maps (1x1 km). A first water supply-to-snow depth ratio for different types of snow has been developed using grid values and data from historical slushflows. [ABSTRACT FROM AUTHOR]

Using the PGA under four exceeding probabilities calculated by China probabilistic seismic hazard analysis method' the probability density function of PGA was obtained by fitting utilizing the Cornell seismic hazard exceeding probability-PGA function model. Combined with the seismic fragility function of the water supply system calculated based on the empirical matrix of actual earthquake damage and the exposure of fixed assets' the seismic loss expectation and loss rate expectation models of the water supply system were established' and the classification standard with the seismic loss rate expectation of the water supply system as the index was proposed. The seismic fragility of the water supply system was classified' and the exposure of the water supply system was analyzed. The total fixed assets in the Water Supply Yearbook were taken as the exposure to earthquake in the region. The accuracy of the fragility model in this paper was verified through the actual earthquake damage losses in Deyang City. Taking the water supply system of 720 cities in Chinese Mainland as an example' the distribution maps of seismic loss expectation and loss rate expectation were calculated and drawn. The loss rate expectation model was verified by the key earthquake prevention areas in Chinese Mainland. The assessment model based on loss expectation and loss rate expectation was taken as the seismic risk assessment model of water supply system in Chinese Mainland. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) and gross primary production (GPP) are critical fluxes contributing to the energy, water, and carbon exchanges between the atmosphere and the land surface. Land surface models such as the Community Land Model v5 (CLM5) quantify these fluxes, estimate the state of carbon budgets and water resources, and contribute to a better understanding of climate change's impact on ecosystems. Past studies have shown the ability of CLM5 to model ET and GPP magnitudes well but emphasized systematic underestimations and lower variability than in the observations. Here, we evaluated CLM5's predictions of water and energy fluxes using observations from eddy covariance stations from the Integrated Carbon Observation System (ICOS), remote sensing, and reanalysis data sets. We assess simulated ET and GPP from the grid scale (CLM5grid) and the plant functional type (PFT) scale (CLM5PFT). CLM5PFT exhibited a low systematic error in simulating the ET at the ICOS sites (average bias of -4.68 %), indicating that PFT-specific ET closely matches the observations' magnitude. GPP was underestimated by CLM5PFT, especially in deciduous forests (bias of -43.76 %). The results showed an underestimation of the spatiotemporal variability in the simulated ET and GPP distribution moments across PFTs for both CLM setups compared to reanalysis data and remote-sensing products. These findings provide essential insights for improving land surface models, highlighting the need to enhance the CLM5's ability to capture the spatiotemporal variability in ET and GPP simulations across PFTs. [ABSTRACT FROM AUTHOR]

Sustainable management of blue and green water resources is vital for the stability and sustainability of watershed ecosystems. Although there has been extensive attention paid to blue water (BW), which is closely related to human beings, the relevance of green water (GW) to ecosystem security is typically disregarded in water resource evaluations. Specifically, comprehensive studies are scarce on the detection and attribution of variations of blue and green water in the Dongjiang River basin (DRB), an important source of regional water supply in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. Here we assess the variations of BW and GW scarcity and quantify the impacts of climate change and land use change on BW and GW in DRB using the multi-water-flux calibrated Soil and Water Assessment Tool (SWAT). Results show that BW and green water storage (GWS) in DRB increased slowly at rates of 0.14 and 0.015 mm a−1, respectively, while green water flow (GWF) decreased significantly at a rate of -0.21 mm a−1. The degree of BW and GW scarcity in DRB is low, and the per capita water resources in more than 80 % of DRB exceed 1700 m3 per capita per year. Attribution results show that 88.0 %, 88.5 %, and 39.4 % of changes in BW, GWF, and GWS result from climate change. Both climate change and land use change have decreased BW, while climate change (land use change) has decreased (increased) GWF in DRB. These findings can guide the optimization of the allocation of blue and green water resources between upper and lower reach areas in DRB and further improve the understanding of blue and green water evolution patterns in humid regions. [ABSTRACT FROM AUTHOR]

In a routine search for meteorological events with a great impact on society in the Extremadura region (southwest interior of the Iberian Peninsula) using newspapers, the month of June 1925 was detected as exceptional due to the large number of thunderstorms associated with significant losses of human lives and material resources. This extraordinary month underwent a detailed examination from various complementary perspectives. Firstly, we reconstructed the history of the events, considering the most impacted locations and the resulting damage. Periodical publications, especially the widely circulated Extremadura newspaper in 1925, were pivotal in this regard. Secondly, we scrutinized monthly meteorological variables (precipitation, temperature, and cloudiness) using the lengthiest-available data series from the Iberian Peninsula. This aimed to underscore the exceptional characteristics of June 1925. Lastly, we analyzed the synoptic situation of the thunderstorm events by employing National Oceanic and Atmospheric Administration/Cooperative Institute for Research in Environmental Sciences/Department of Energy (NOAA/CIRES/DOE) 20th Century Reanalysis V3 (20CR) data. This approach allowed us to comprehend, from a synoptic perspective, the exceptional nature of this month. Thereby, a combination of a negative North Atlantic Oscillation (NAO) situation, elevated convective available potential energy (CAPE) values, large-scale lifting, and abundant precipitable water availability in the region was revealed. [ABSTRACT FROM AUTHOR]

Green water (terrestrial evapotranspiration) flows from source regions, precipitates downwind via moisture recycling, recharges water resources, and sustains the social economy in sink regions. However, unlike blue water, there has been limited assessment of green water flows and their teleconnected effects on the social economy. This study used a climatology mean moisture trajectory dataset produced by the UTrack model for 2008–2017 to quantify interprovincial green water flows in China and their socioeconomic contributions. Results reveal an interconnected flow network where the green water of each province reciprocally exchanges with each other. Despite self-recycling (ranging from 0.6 % to 35 %), green water mainly forms precipitation in neighboring provinces, with average interprovincial flow directions from west to east and south to north. About 56 % of the total green water exported from the 31 mainland source provinces remains at home, contributing 43 % of the precipitation in China. The green water from the source provinces embodies substantial socioeconomic value for the downwind provinces, accounting for about 40 % of the water resources, 45 % of the gross domestic product (GDP), 46 % of the population, and 50 % of the food production of China. Green water from the western provinces is the largest contributor to water resources, while green water from the southwestern and central provinces embodies the highest GDP, population, and food production. Overall, the embodied socioeconomic values of green water flow increase from the source to sink provinces, suggesting that green water from less developed provinces effectively supports the higher socioeconomic status of developed provinces. This assessment emphasizes the substantial teleconnected socioeconomic values of green water flows and the need to incorporate them towards more comprehensive and effective water resource management. [ABSTRACT FROM AUTHOR]

A large storm surge caused by Hurricane Dorian in 2019 resulted in extensive flooding and saltwater intrusion into the aquifers of the island of Grand Bahama. This caused about 40 % of the island's water supply to become brackish with no or slow recovery to date and damage to more than 70 % of mangroves and forests on Grand Bahama. Managed aquifer recharge (MAR) and reforestation were considered nature-based solutions (NBS) to mitigate the impacts of Hurricane Dorian. First, a technical assessment of MAR investigated (hydro)geological aspects. As a result, potential locations for a MAR scheme are proposed. Further, a financial and an extended cost–benefit analysis (CBA) integrating ecosystem service (ES) assessments are conducted for proposed MAR and reforestation measures. Based on the current data availability, results indicate that the MAR scheme of rooftop rainwater harvesting is technically feasible. However, based on our first estimate with limited data, this MAR scheme will be able to provide only about 10 % of the water demand in the study area and thus would not be favorable from a financial perspective. Since MAR has a range of positive aspects (including potential reduction of desalinization efforts and improvement freshwater-dependent ecosystems), we recommend reassessment with more detailed hydrogeological data. On the other hand, reforestation measures are assessed to be financially profitable. The results of this study not only prove the technical feasibility and the added value of restoring groundwater and the forest ecosystem on Grand Bahama, but also highlight the associated high costs. The developed methods for investigating ecosystem services from an economic perspective was proven to allow for a systematic comparison of NBSs and reverse osmosis costs and benefits helping, e.g., policy- and decision-makers and to justify their implementation. [ABSTRACT FROM AUTHOR]

It is crucial to improve global precipitation estimates for a better understanding of water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using the algorithm of the local ensemble transform Kalman filter (LETKF), a computationally efficient ensemble data assimilation method, in which the first guess and its error covariance are developed based on the reanalysis data of precipitation from the European Centre for Medium-Range Weather Forecasts (ERA5). For the estimation for each date, the climatological ensembles are constructed using the ERA5 data 10 years before and after that date, and thereafter they are utilized to obtain the first guess and its error covariance. Additionally, the global rain gauge observations provided by the National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) are used for observation inputs in the LETKF algorithm. Our estimates have better agreements with independent rain gauge observations compared to the existing precipitation estimates of the NOAA CPC in general. Because we utilized the same rain gauge observations for the inputs of our estimation as those used in the NOAA CPC product, this indicates that the proposed estimation method is superior to that of the NOAA CPC (i.e., optimal interpolation). Our proposed method had the advantage of constructing a physically consistent first guess and its error variance using reanalysis data for interpolating precipitation fields. Furthermore, validations against independent rain gauge observations showed that our estimates are largely improved in mountainous or rain-gauge-sparse regions compared to the CPC estimates, indicating strong benefits of the proposed method for such regions. [ABSTRACT FROM AUTHOR]

Runoff originating from ground ice contained in rock glaciers represents a significant water supply for lowlands. Pseudo-relict rock glaciers contain patchy permafrost but appear to be relict, and therefore they can be misinterpreted when using standard classification approaches. The permafrost content, spatial distribution and frequency of this type of rock glacier are poorly known. Therefore, identifying pseudo-relict rock glaciers that might still contain permafrost, and potentially ice, is crucial for understanding their hydrological role in a climate change context. This work analyses rock–glacier spring-water temperature in a 795 km2 catchment in the eastern Italian Alps to understand how many rock glaciers classified as relict could have spring-water temperatures comparable to active or transitional rock glaciers as possible evidence of their pseudo-relict nature. Spring-water temperature, often auxiliary to other approaches for specific sites, was used for a preliminary estimate of the permafrost presence in 50 rock glaciers classified as relict. In addition, we present electrical resistivity tomography (ERT) results on two relict rock glaciers with opposing spring-water temperature and surface characteristics to constrain spring-water temperature results at the local scale. The results show that about 50 % of the rock glaciers classified as relict might be pseudo-relict, thus potentially containing permafrost. Both supposedly relict rock glaciers investigated by geophysics contain frozen sediments. The majority of the cold springs are mainly associated with rock glaciers with blocky and scarcely vegetated surfaces, but geophysics suggest that permafrost may also exist in rock glaciers below 2000 m a.s.l., entirely covered by vegetation and with a spring-water temperature of up to 3.7 °C. We estimate that pseudo-relict rock glaciers might contain a significant portion (20 %) of all the ice stored in the rock glaciers in the study area. These results highlight the relevance of pseudo-relict rock glaciers in periglacial environments. Even if not a conclusive method, spring-water temperature analyses can be used to preliminarily distinguish between relict and pseudo-relict rock glaciers in wide regions. [ABSTRACT FROM AUTHOR]

By considering the increasing trend in water consumption and significant reduction of water resources in most countries of the world, groundwater resources have become very important. The Target of this study is to implement machine learning models to produce a groundwater potential map (GWPM), identify areas with higher water potential, and also identify influencing factors. Therefore, two algorithms including the random forest (RF) and support vector regression (SVR), were performed that according to the literature have a good compatibility with this type of problems, compared to the other models. Of the 351 well points available throughout the study area, 70% (245 well points) were selected as the target for training the models and the rest 30% (106 well points) were used for evaluating the models. In addition, 20 effective information layers were used for modeling. In this study, an effort was made to focus more on data preparation that is one of the most important parts of model development. The variance inflation factor (VIF) and correlation coefficient were applied to identify the dependent variables. Also, feature selection was done to identify the most influential factors. Finally, two groundwater potential map(GWPM)s were created based on these two models. By calculating the area under the curve (AUC) from the receiver operating characteristic (ROC), the prediction accuracy of the two models was calculated. The values for AUC of the two maps produced by the RF and SVR algorithms were 93.4% and 89.7%, respectively. This study improves the knowledge of groundwater potential in the study area which is one of the cities with water scarcity in the country. [ABSTRACT FROM AUTHOR]

Streamflow drought hazard indicators (SDHI) are mostly lacking in large-scale drought early warning systems (DEWS). This paper presents a new systematic approach for selecting and computing SDHI for monitoring drought risk for human water supply from surface water and for river ecosystems that is also relevant for meteorological or soil moisture drought. We recommend considering the habituation of people and ecosystems to the streamflow regime (e.g., a certain interannual variability or relative reduction of streamflow) when selecting indicators. Distinguishing four indicator types, we classify indicators of drought magnitude (water deficit during a pre-defined period) and severity (cumulated magnitude since onset of the drought event). We quantify eight existing and three new SDHI globally using the global hydrological model WaterGAP2.2d. We recommend streamflow hazard indicators that should be included in large-scale DEWS as they are suitable for risk systems that are differently adapted to low water availability and characterized by either perennial or intermittent streamflow regime and the existence or not of large reservoirs. Drought magnitude is best quantified by return period or relative deviation from mean, and severity by return period or water volume below a threshold relative to mean annual streamflow. Both anomaly and deficit indicators should be provided. [ABSTRACT FROM AUTHOR]

The prediction of water resource evolution is considered to be a major challenge for the coming century, particularly in the context of climate change and increasing demographic pressure. Water resources are directly linked to the continental water cycle and the main processes modulating changes can be represented by global hydrological models. However, anthropogenic impacts on water resources, and in particular the effects of dams-reservoirs on river flows, are still poorly known and generally neglected in 5 coupled land surface - river routing models. This paper presents a parameterized reservoir model, DROP (Dam-Reservoir OPeration model), based on Hanasaki's scheme to compute monthly releases given inflows, water demands and the management purpose. With its significantly anthropized river basins, Spain has been chosen as a study case for which simulated outflows and water storage variations are evaluated against in situ observations over the period 1979-2014. Using a default configuration of the reservoir model, results reveal its positive contribution in representing the seasonal cycle of dis10 charge and storage variation, specifically for large-storage capacity irrigation reservoirs. Based on a bounded version of the Nash-Sutcliffe Efficiency (NSE) index, called C2M, the overall outflow representation is improved by 43% in the median. For irrigation reservoirs, the improvement rate reaches 80%. A comprehensive sensitivity analysis of DROP model parameters was conducted based on the performance of C2M on outflows and volumes using the Sobol method. The results show that the most influential parameter is the threshold coefficient describing the demand-controlled release level. The analysis also reveals the 15 parameters that need to be focused on in order to improve river flow or reservoir water storage modeling by highlighting the difference in the individual effects of the parameters and their interactions depending on whether one focuses on outflows or volume mean seasonal patterns. The results of this generic reservoir scheme show promise for modeling present and future reservoir impacts on the continental hydrology within global land surface - river routing models. [ABSTRACT FROM AUTHOR]

Life in hyperarid regions has adapted to extreme water scarcity through mechanisms like salt deliquescence. While halite (NaCl) crusts have been intensively studied and identified as one of the last habitats under hyperarid conditions, other less common hygroscopic salt crusts remain unexplored. Here, we investigated newly discovered deliquescent soil surfaces in the Atacama Desert, containing substantial amounts of nitrates, to evaluate their habitability for microorganisms. We characterized the environment with respect to water availability and biogeochemistry. Microbial abundances and composition were determined by cell cultivation experiments, 16S rRNA gene sequencing, and membrane phospholipid fatty acid (PLFA) analysis, while microbial activity was assessed by analyzing adenosine triphosphate (ATP) and the molecular composition of organic matter. Our findings reveal that, while the studied hygroscopic salts provide temporary water, microbial abundances and activity are lower in the studied soil surfaces than in non-deliquescent soil surfaces. Intriguingly, the deliquescent crusts are enriched in geochemically degraded organic matter, indicated by the molecular composition. We conclude that high nitrate concentrations in the hyperarid soils suppress microbial activity but preserve eolian-derived biomolecules. These insights are important for assessing the habitability and searching for life in hyperarid environments on Earth and beyond. [ABSTRACT FROM AUTHOR]

Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity. [ABSTRACT FROM AUTHOR]

As drought and prolonged water stress become more prevalent in dry regions under climate change, preserving water resources becomes a focal point for maintaining forest health. Forest regeneration after forest loss or disturbance can lead to overstocked juvenile stands with high water demands and low water-use efficiency. Forest thinning is a common practice with the goal of improving tree health, carbon storage, and water use while decreasing stand demands in arid and semi-arid regions. However, little is known about the impacts of stand density on seasonal variation in depth-to-water uptake or the magnitude of the effect of growing season drought conditions on water availability. Existing reports are highly variable by climatic region, species, and thinning intensity. In this study, stable isotope ratios of deuterium (δ2 H) and oxygen (δ18 O) in water collected from various soil depths and from branches of lodgepole pine (Pinus contorta) under different degrees of thinning (control: 27 000 stems per hectare; moderately thinned: 4500 stems per hectare; heavily thinned: 1100 stems per hectare) over the growing season were analyzed using the MixSIAR Bayesian mixing model to calculate the relative contributions of different water sources in the Okanagan Valley in the interior of British Columbia, Canada. We found that under drought conditions the lodgepole pine trees shifted their depth-to-water uptake through the growing season (June to October) to rely more heavily on older precipitation events that percolated through the soil profile when shallow soil water became less accessible. Decreased forest density subsequent to forest thinning did not cause a significant difference in the isotopic composition of branch water but did cause changes in the timing and relative proportion of water utilized from different depths. Thinned lodgepole pine stands were able to maintain water uptake from 35 cm below the soil profile, whereas the overstocked stands relied on a larger proportion of deep soil water and groundwater towards the end of the growing season. Our results support other findings by indicating that, although lodgepole pines are drought-tolerant and have dimorphic root systems, they do not shift back from deep water sources to shallow soil water when soil water availability increases following precipitation events at the end of the growing season. [ABSTRACT FROM AUTHOR]

Subsurface non-isothermal fluid injection is a ubiquitous scenario in energy and water resource applications, which can lead to geochemical disequilibrium and thermally driven solubility changes and reactions. Depending on the nature of the solubility of a mineral, the thermal change can lead to either mineral dissolution or precipitation (due to undersaturation or supersaturation conditions). Here, by considering this thermo-hydro-chemical (THC) scenario and by calculating the temperature-dependent solubility using a non-isothermal solution (the so-called Lauwerier solution), thermally driven reactive transport solutions are derived for a confined aquifer. The coupled solutions, hereafter termed the "reactive Lauwerier problem", are developed for axisymmetric and Cartesian symmetries and additionally provide the porosity evolution in the aquifer. The solutions are then used to study two common cases: (I) hot CO2-rich water injection into a carbonate aquifer and (II) hot silica-rich water injection into a sandstone aquifer, leading to mineral dissolution and precipitation, respectively. We discuss the timescales of such fluid–rock interactions and the changes in hydraulic system properties. The solutions and findings contribute to the understanding and management of subsurface energy and water resources, such as aquifer thermal energy storage, aquifer storage and recovery and reinjection of used geothermal water. The solutions are also useful for developing and benchmarking complex coupled numerical codes. [ABSTRACT FROM AUTHOR]

Here we present a strategy to obtain a reliable hydrological simulation over France with the ORCHIDEE land surface model. The model is forced by the SAFRAN atmospheric reanalysis at 8 km resolution and hourly time steps from 1959 to 2020 and by a high-resolution DEM (around 1.3 km in France). Each SAFRAN grid cell is decomposed into a graph of hydrological transfer units (HTUs) based on the higher-resolution DEM to better describe lateral water movements. In particular, it is possible to accurately locate 3507 stations among the 4081 stations collected from the national hydrometric network HydroPortail (filtered to drain an upstream area larger than 64 km 2). A simple trial-and-error calibration is conducted by modifying selected parameters of ORCHIDEE to reduce the biases of the simulated water budget compared to the evapotranspiration products (the GLEAM and FLUXCOM datasets) and the HydroPortail observations of river discharge. The simulation that is eventually preferred is extensively assessed with classic goodness-of-fit indicators complemented by trend analysis at 1785 stations (filtered to have records for at least 8 entire years) across France. For example, the median bias of evapotranspiration is -0.5 % against GLEAM (-4.3 % against FLUXCOM), the median bias of river discharge is 6.3 %, and the median Kling–Gupta efficiency (KGE) of square-rooted river discharge is 0.59. These indicators, however, exhibit a large spatial variability, with poor performance in the Alps and the Seine sedimentary basin. The spatial contrasts and temporal trends of river discharge across France are well represented with an accuracy of 76.4 % for the trend sign and an accuracy of 62.7 % for the trend significance. Although it does not yet integrate human impacts on river basins, the selected parameterization of ORCHIDEE offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of spatiotemporal runoff changes in the Yarlung Zangbo (YZ) basin in the southern Tibetan Plateau (TP) at a sub-basin scale, amidst varying climatic and cryospheric conditions, is imperative for effective water resources management. However, spatiotemporal differences of runoff composition and change and their attribution within the YZ basin have not been extensively explored, primarily due to the lack of hydrometeorological observations, especially in the downstream region. In this study, we investigated historical and future evolution of annual and seasonal total water availability, as well as glacier runoff and snowmelt contributions across six sub-basins of the YZ, with a particular focus on the comparison between the upstream Nuxia (NX) basin and the downstream Nuxia–Pasighat (NX-BXK) basin, based on a newly generated precipitation dataset and a well-validated model with streamflow, glacier mass, and snow cover observations. Our findings revealed that large spatiotemporal differences in changes exist within the YZ basin for 1971–2020. Firstly, runoff generation was dominated by rainfall runoff throughout the YZ basin, with glacier runoff playing a more important role in the annual total runoff (19 %) in the NX-BXK sub-basin compared to other sub-basins. Notably, glacier runoff contributed 52 % of the total runoff at the Pasighat outlet of the YZ basin. Secondly, annual runoff exhibited an increasing trend in the NX basin but a decreasing trend in the NX-BXK, primarily attributed to rainfall runoff changes influenced by atmospheric moisture. Glacier runoff enhanced water supply by offsetting the decreasing contribution from rainfall. Total runoff will consistently increase (27–100 mm (10 yr) -1) across the sub-basins through the 21st century, resulting from increased rainfall runoff and a minor effect of increased snowmelt and glacier runoff. [ABSTRACT FROM AUTHOR]

The aerosol–cloud–precipitation interactions within the cloud-topped marine boundary layer (MBL) are examined using aircraft in situ measurements from Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) field campaigns. SOCRATES clouds exhibit a larger number concentration and smaller cloud droplet effective radius (148.3 cm-3 and 8.0 µm) compared to ACE-ENA summertime (89.4 cm-3 and 9.0 µm) and wintertime clouds (70.6 cm-3 and 9.8 µm). The ACE-ENA clouds, especially during the winter, feature stronger drizzle formation via droplet growth through enhanced collision–coalescence that is attributed to a relatively cleaner environment and deeper cloud layer. Furthermore, the aerosol–cloud interaction (ACI) indices from the two aircraft field campaigns exhibit distinct sensitivities, indicating different cloud microphysical responses to aerosols. The ACE-ENA winter season features relatively fewer aerosols, which are more likely activated into cloud droplets under the conditions of sufficient water vapor availability and strong turbulence. The enriched aerosol loading during ACE-ENA summer and SOCRATES generally leads to smaller cloud droplets competing for the limited water vapor and exhibiting a stronger ACI. Notably, the precipitation susceptibilities are stronger during the ACE-ENA than during the SOCRATES campaigns. The in-cloud drizzle behavior significantly alters sub-cloud cloud condensation nuclei (CCN) budgets through the coalescence-scavenging effect and, in turn, impacts the ACI assessments. The results of this study can enhance understanding and aid in future model simulation and assessment of the aerosol–cloud interaction. [ABSTRACT FROM AUTHOR]

Meteorological and hydrological hazards present challenges to people and ecosystems worldwide, but the limited length of observational data means that the possible extreme range is not fully understood. Here, a large ensemble of climate model data is combined with a simple grid-based hydrological model to assess unprecedented but plausible hydrological extremes in the current climate across England. Two case studies are selected – dry (summer 2022) and wet (autumn 2023) – with the hydrological model initialised from known conditions and then run forward for several months using the large climate ensemble. The modelling chain provides a large set of plausible events including extremes outside the range from use of observed data, with the lowest flows around 28 % lower on average for the summer 2022 drought study and the highest flows around 42 % higher on average for the autumn 2023 flood study. The temporal evolution and spatial dependence of extremes are investigated, including the potential timescale of the recovery of flows to normal levels and the chance of persistent extremes. Being able to plan for such events could help improve the resilience of water supply systems to drought and improve flood risk management and incident response. [ABSTRACT FROM AUTHOR]

The extensive snow cover across the Tibetan Plateau (TP) has a major influence on the climate and water supply for over 1 billion downstream inhabitants. However, an adequate evaluation of variability in the snow cover fraction (SCF) over the TP simulated by multiple reanalysis datasets has yet to be undertaken. In this study, we used the Snow Property Inversion from Remote Sensing (SPIReS) SCF dataset for the water years (WYs) 2001–2017 to evaluate the capabilities of eight reanalysis datasets (HMASR, MERRA2, ERA5, ERA5L, JRA55, CFSR, CRAL, and GLDAS) in simulating the spatial and temporal variability in SCF in the TP. CFSR, GLDAS, CRAL, and HMASR are good in simulating the spatial pattern of climatological SCF, with lower bias and higher correlation and Taylor skill score (SS). By contrast, ERA5L, JRA55, and ERA5 have a relatively good performance in terms of SCF annual trends among eight reanalysis datasets. The biases in SCF simulations across reanalysis datasets are influenced by a combination of meteorological forcings, including snowfall and temperature, as well as by the SCF parameterization methods. However, the primary influencing factors vary among the reanalysis datasets. Additionally, averaging multiple reanalysis datasets can enhance the spatiotemporal accuracy of SCF simulations, but this enhancement effect does not consistently increase with the number of reanalysis datasets used. [ABSTRACT FROM AUTHOR]

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World , based on Sentinel-2, and the NASA IMPACT model , based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via 10.25739/03nt-4f29. [ABSTRACT FROM AUTHOR]

Reservoirs are ubiquitous water infrastructure, providing functional capability to manage, and often mitigate, hydrological variability across space and time. The presence and operation of a reservoir control the downstream flow regime, such that in many locations understanding reservoir operations is crucial to understanding the hydrological functioning of a catchment. Despite many advances in modelling reservoir operations, inclusion of reservoirs in large-scale hydrological modelling remains challenging, particularly when the number of reservoirs is large and data access is limited. Here we design a set of simple reservoir operating rules (with only two calibrated parameters) focused on simulating small water supply reservoirs across large scales with various types of open-access data (i.e. catchment attributes and flows at downstream gauges). We integrate our rules into a national-scale hydrological model of Great Britain and compare hydrological simulations with and without the new reservoir component. Our simple reservoir operating rules significantly increase model performance in reservoir-impacted catchments, particularly when the rules are calibrated individually at each downstream gauge. We also test the feasibility of using transfer functions (which transform reservoir and catchment attributes into operating rule parameters) to identify a nationally consistent calibration. This works well in ∼ 50 % of the catchments, while nuances in individual reservoir operations limit performance in others. We suggest that our approach should provide a lower benchmark for simulations in catchments containing water supply reservoirs and that more complex methods should only be considered where they outperform our simple approach. [ABSTRACT FROM AUTHOR]

The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. Thus, understanding runoff generation processes of Alpine streams is important for sustainable water management. It is currently unclear how much streamflow is derived from old water stored in the subsurface and how much stems from more recent precipitation that reaches the stream via near-surface quick flow processes. It is also unclear how this partitioning varies across different Alpine catchments in response to hydroclimatic forcing and catchment characteristics. Here, we use stable water isotope time series in precipitation and streamflow to quantify the young water fractions (Fyw ; i.e., the fraction of water younger than approximately 2–3 months) and new water fractions (Fnew ; here, the fraction of water younger than 1 month) in streamflow from 32 Alpine catchments. We contrast these measures of water age between summer and winter and between wet and dry periods and then correlate them with hydroclimatic variables and physical catchment properties. New water fractions varied between 3.5 % and 9.6 %, with values of 9.2 % in rainfall-dominated catchments, 9.6 % in hybrid catchments, and 3.5 % in snow-dominated catchments (mean across all catchments of 7.1 %). Young water fractions were approximately twice as large (reflecting their longer timescale) and ranged between 10.1 % and 17.6 %, with values of 17.6 % in rainfall-dominated catchments, 16.6 % in hybrid catchments, and 10.1 % in snow-dominated catchments (mean across all catchments of 14.3 %). New water fractions were negatively correlated with catchment size (Spearman rank correlation, rS , of -0.38), q95 baseflow (rS=-0.36), catchment elevation (rS=-0.37), total catchment relief (rS=-0.59), and the fraction of slopes steeper than 40° (rS=-0.48). Large new water fractions, implying faster transmission of precipitation to streamflow, are more prevalent in small catchments, at low elevations, with small elevation differences, and with large fractions of forest cover (rS=0.36). New water fractions averaged 3.3 % following dry antecedent conditions, compared with 9.3 % after wet antecedent conditions. Our results quantify how hydroclimatic and physical drivers shape the partitioning of old and new waters across the Alps, thus indicating which landscapes transmit recent precipitation more readily to streamflow and which landscapes tend to retain water over longer periods. Our results further illustrate how new water fractions may find relationships that remained invisible with young water fractions. [ABSTRACT FROM AUTHOR]

Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given ongoing climate change impacts. Synthetic aperture radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow, and SAR interferometry (InSAR) can be used to estimate changes in SWE (Δ SWE) between SAR acquisitions. We calculated Δ SWE retrievals from 10 NASA L-band (1–2 GHz, ∼25 cm wavelength) uninhabited aerial vehicle SAR (UAVSAR) acquisitions covering a ∼640 km 2 swath in northern Colorado during the winters of 2020 and 2021. UAVSAR acquisitions coincided with ∼117 mm of accumulation in 2020 and ∼282 mm of accumulation in 2021. Δ SWE retrievals were evaluated against measurements of SWE from repeat ground-penetrating radar (GPR) and terrestrial lidar scans (TLSs) collected during the NASA SnowEx time series campaigns at two field sites (total area =∼0.2 km 2) as well as SWE measurements from seven automated stations distributed throughout the UAVSAR swath. For single InSAR pairs, UAVSAR Δ SWE retrievals yielded an overall r of 0.72–0.79 and an RMSE of 19–22 mm when compared with TLS and GPR Δ SWE retrievals. UAVSAR Δ SWE showed some scatter with Δ SWE measured at automated stations for both study years, but cumulative UAVSAR SWE yielded a r of 0.92 and an RMSE of 42 mm when compared to total SWE measured by the stations. Further, UAVSAR Δ SWE RMSEs differed by <10 mm for coherences (i.e., the complex interferometric coherence) of 0.10 to 0.90, suggesting that coherence has only a small influence on the Δ SWE retrieval accuracy. Given the evaluations presented here and in other recent studies, the upcoming NASA-ISRO SAR (NISAR) satellite mission, with a 12 d revisit period, offers an exciting opportunity to apply this methodology globally. [ABSTRACT FROM AUTHOR]

Water-related conflicts in river catchments occur due to both internal and external pressures that affect catchment water availability. Lack of common understanding of human–water perspectives by catchment stakeholders increases the complexity of human–water issues at the river catchment scale. Among a range of participatory approaches, the development and use of serious games gained prominence as a tool to stimulate discussion and reflection among stakeholders about sustainable resource use and collective action. This study designed and implemented the ENGAGE (Exploring New Gaming Approach to Guide and Enlighten) game that mimics the dynamics observed during the dry season in the upper Ewaso Ngiro catchment, northwest of Mount Kenya. The purpose of this study was to explore the potential role of serious gaming in subsequent steps of strengthening stakeholder engagement (agenda setting, shared understanding, commitment to collective action, and means of implementation) toward addressing complex human–water challenges at the catchment scale. We assessed the type of decisions made during gameplay, the communication dynamics, the active participation, and the implication of decisions made on water availability. The results of three game sessions show that the ENGAGE game raised awareness and provided a recognizable hydrologic background to conflicts while guiding community discussions toward implementable decisions. The results revealed increasing active participation, knowledge gain, and use of plural pronouns and decreasing individual interests and conflicts among game participants. This study presents important implications for creating a collective basis for water management and can inform human–water policies and modification of the process behind water allocation rules in a river catchment. [ABSTRACT FROM AUTHOR]